Class Overview
What will we do?
This class is focused on the data validator challenge, a use case for automating poor quality data and governance over that process by identifying required review cases. The challenge is based on reviewing an older manual entry system for purchase orders and providing the client with a new system.
Materials and Preparation
Class Materials
- Link: The worst table so far to be scraped.
- Link: Output Template for the example table (convert from csv to google sheet).
- Link: UiPath Studio web starter file for the data validator challenge.
- Link: UiPath's Community Forum (another place for finding potential solutions).
- Slides: PowerPoint or PDF
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Suggested Pre-Class Preparation
- Before class or at the beginning of class create a subfolder in your google drive for this process called data_validator_challenge.
- Save and convert the csv (link above) to a google sheet in this folder (or copy the headers to a blank google sheet and call it output_test_case.
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Class Plan
- After a brief review and introduction to the case, we will begin working on the data validation challenge.
- connect the google sheet to your workflow. All the raw data will be read in, and placeholders for the clean data have been created in the workflow in advance.
- Step 1 of the challenge is to examine the test case dataset, which is the worst table so far (link above), and scrape it into the workflow.
- For each field, create a cleaned output. For the Table, add a boolean (True/False) column called exception_flag, a column called review_comments, and a column called routing_status.
- Process the input spreadsheet row by row using a rule that can check that the data is valid, clean the data. If the data is clean, set exception_flag to False and change routing_status to autovalidated.
- If the data is not valid, raise an exception_flag by changing exception_flag to True, and add comments to the review_comments column if something needs review (remember this comment needs to be general), and change status to pending review.
- Try to be as general as possible with your solutions for the given human errors in the example data, as it may not include all potential human errors.
- You can use any LLM assistance that you want, as well as the UiPath forum. LLMs will do a better job assisting you if you ask explicitly about VB formula for an assign (otherwise it may respond with confusing notation for tools and syntax).
- Try and finish the case in class, but note that you have until Monday's class to complete the challenge. Once you have completed the test case data, at the beginning of Monday's class you will be provided with a spreadsheet of data to test your automated data validator against, the goal is to catch all of the problem cases, sorting out any entries that require a review from those that do not.